This invention is generally directed to a method and apparatus for assaying a drug candidate and, more specifically, to a method for measuring the binding interaction between a drug candidate and sensing surface-bound biomolecules of a biosensor to determine a binding interaction parameter of the drug candidate, and then comparing the binding interaction parameter against a predetermined drug correlation graph (e.g., a mathematical expression) to estimate at least one pharmacokinetic parameter.
A variety of experimental techniques are currently used to determine chemical, physical and biological properties associated with low molecular weight substances, particularly in the context of drug discovery. For example, researchers are often concerned with determining a variety of chemical, physical and biological properties associated with drug candidates for screening purposes. The determination of such properties often plays a pivotal role in the drug development and screening process.
More specifically, it has long been recognized that the intensity and duration of the pharmacological effect of a systemically acting drug are functions not only of the intrinsic activity of the drug, but also of its absorption, distribution, metabolism, and excretion (ADME) characteristics within the human body. These so-called ADME characteristics are all intimately related to the scientific discipline known as xe2x80x9cpharmacokinetics.xe2x80x9d Pharmacokinetics is commonly referred to as the study of the time courses (i.e., kinetics) associated with the dynamic processes of ADME of a drug and/or its metabolites within a living organism, and is closely interrelated with the fields of biopharmaceutics, pharmacology, and therapeutics.
Because the body delays the transport of drug molecules across membranes, dilutes them into various compartments of distribution, transforms them into metabolites, and eventually excretes them, it is often difficult to accurately predict the pharmacological effect of promising new drug candidates. Researchers, however, commonly use pharmacokinetic ADME studies as one method to predict the efficacy of a drug at a site of action within the body.
Traditionally, researchers involved with preclinical ADME studies have used pharmacokinetic/mathematical models coupled with actual drug concentration data from blood (or serum or plasma) and/or urine, as well as concentration data from various tissues, to characterize the behavior and xe2x80x9cfatexe2x80x9d of a drug within living organisms. As is appreciated by those skilled in the art, the mathematical equations associated with pharmacokinetics are generally based on models that conceive the body as a multicompartmental organism. In such models it is presumed that the drug and/or its metabolites are equitably dispersed in one or several fluids/tissues of the organism. Any conglomerate of fluid or tissue which acts as if it is kinetically homogeneous may be termed a xe2x80x9ccompartment.xe2x80x9d Each compartment acts as an isotropic fluid in which the molecules of drug that enter are homogeneously dispersed and where kinetic dependencies of the dynamic pharmacokinetic processes may be formulated as functions of the amounts or concentrations of drug and metabolites therein. Stated somewhat differently, the conceptual compartments of the body are separated by barriers that prevent the free diffusion of drug among them; the barriers are kinetically definable in that the rate of transport of drug or metabolite across membrane barriers between compartments is a function of, for example, the amounts or concentrations of drug and metabolites in the compartments, the permeability of various membranes, and/or the amount of plasma protein binding and general tissue binding.
More specifically, pharmacokinetic/mathematical models are commonly used by pharmacokineticists to represent drug absorption, distribution, metabolism, and excretion as functions of time within the various tissues and organs of the body. In such models, the movement of the administered drug throughout the body is concisely described in mathematical terms (e.g., a set of differential equations). The predictive capability of such models lies in the proper selection and development of mathematical functions that parameterize the essential factors governing the kinetic process under consideration.
For example, a drug that is administered by intravenous injection may be assumed to distribute rapidly in the bloodstream. A pharmacokinetic/mathematical model that describes this situation may be a tank containing a volume of fluid that is rapidly equilibrated with the drug. Because a fraction of the drug in the body is continually eliminated as a function of time (e.g., excreted by the kidneys and metabolized by the liver), the concentration of the drug in the hypothetical tank may be characterized by two parameters: (1) the volume of fluid in the tank that will dilute the drug, and (2) the elimination rate of the drug per unit of time, both of which are generally considered to be constant. Thus, if a known set of drug concentrations in the tank is determined at various time intervals by, for example, sampling, then the volume of fluid in the tank and rate of drug elimination may be estimated. This information may then, in turn, be used for predicting the disposition of the drug within a human body.
Theoretically, an unlimited number of models may be constructed to describe the kinetic processes of drug absorption, distribution, metabolism, and excretion within the various tissues and organs of the human body. In general, however, the number of useful models is limited due to practical considerations associated with blood, tissue and/or organ sampling. As a result, and as is appreciated by those skilled in the art, two major types of models have been developed by pharmacokineticists: (1) compartmental models; and (2) physiologic models.
In pharmacokinetic compartmental models, the body is represented as a series of compartments that communicate reversibly with each other. Each compartment is not a real physiological or anatomic region; rather, each compartment is considered to be inclusive of all tissues that have similar blood flow and drug affinity. For example, a compartmental model may consist of one or more peripheral compartments representing tissue(s) connected to a central compartment representing the blood stream. Conceptually, the drug moves dynamically into and out of the central compartment and into and out of each of the peripheral compartments. As such, rate constants may be used to represent the overall rate process for the drug""s disposition within each compartment. Compartment models are generally based on linear assumptions using linear differential equations, and are particularly useful when there is little information known about the tissues and their respective drug concentrations.
In contrast, pharmacokinetic physiologic models are based on known anatomic and physiologic data, data which is kinetically described in view of the actual blood flow volumes responsible for distributing the drug to the various parts of the body. Because there are many tissue organs in the body, each tissue volume must be estimated and its drug concentration and rate of change described mathematically (tissues having similar blood perfusion properties, however, are typically grouped together). Unfortunately, much of the information required to adequately describe such pharmacokinetic physiologic models are often very difficult to obtain experimentally. Nevertheless, such physiologically based models are commonly used in conjunction with animal data and interspecies scaling techniques to predict the drug""s disposition within a human body.
More importantly, however, is that pharmacokinetic/mathematical models, and knowledge of their associated ADME parameters play an extremely important role in drug discovery and development. A typical example is a drug that is active following intravenous administration but is considerably less active after comparable oral doses. Having appropriate pharmacokinetic information may reveal (1) whether the drug was poorly absorbed to yield subtherapeutic circulating levels, or (2) whether the drug experienced presystemic metabolism to an inactive metabolite. Such information may also provide guidance for subsequent decisions, such as (1) whether to improve drug absorption by altering the salt form or formulation, (2) whether to investigate the possibility of making prodrugs, or (3) whether to consider a parenteral route of administration.
In addition to the foregoing, pharmacokinetic/mathematical models are also generally considered extremely useful for, among other things: (1) predicting plasma, tissue, and urine drug levels with any dosage regimen; (2) calculating the optimum dosage regimen for an individual patient; (3) estimating the possible accumulation of drugs and/or metabolites; (4) correlating drug concentrations with pharmacologic and toxicologic activity (i.e., pharmacodynamics); (5) evaluating differences in the rate or extent of availability between formulations (i.e., bioequivalence); (6) describing how changes in physiology or disease affect the absorption, distribution, and/or elimination of the drug; and (7) explaining drug-drug and food-drug interactions.
Lastly, pharmacokinetic ADME data has also become an integral part of the pharmacological characterization process of promising new drug candidates. Regulatory agencies, such as the U.S. Food and Drug Administration, now require (1) a determination of pharmacokinetic ADME data in Phase I drug studies, and (2) a submission of pharmacokinetic ADME data as part of a New Drug Application. In this context, such pharmacokinetic ADME data is deemed essential for predicting the behavior and fate of the drug candidate within the human body.
Accordingly, there is a need in the art for improved methods for determining one or more pharmacokinetic parameters associated with absorption, distribution, metabolism, and excretion of a drug candidate. There is also a need for apparatuses useful for carrying out such methods. The present invention fulfills these needs and provides further related advantages.
In brief, the present invention is directed to a method and apparatus for assaying a drug candidate. More specifically, this invention discloses a method for measuring the binding interaction between a drug candidate and sensing surface-bound biomolecules of a biosensor to determine a binding interaction parameter of the drug candidate, and then comparing the binding interaction parameter against a predetermined drug correlation graph to estimate at least one pharmacokinetic parameter of the drug candidate. The at least one pharmacokinetic parameter may, for example, be one or more of ADME.
In another embodiment of the present invention, at least two pharmacokinetic parameters of the drug candidate are determined, and in yet another embodiment, at least one pharmacokinetic parameter and a solubility property of the drug candidate are determined. Such pharmacokinetic parameters and/or solubility property may be determined when the one or more sensing surface-bound biomolecules are selected from, for example, liposomes, plasma proteins, CYP 450 enzymes, metabolic enzymes, or transport proteins.
The biosensor used in the practice of the present invention may utilize a mass-sensing technique, such as surface plasmon resonance. In addition, the biosensor may further employ a sensor chip, wherein the sensor chip comprises a free electron metal that includes a sensor surface, wherein the free electron metal is copper, silver, aluminum or gold. The sensor chip may further comprise a hydrogel coupled to the sensor surface, wherein the hydrogel has a plurality of functional groups, and wherein the one or more sensing surface-bound biomolecules are covalently bonded to the hydrogel.
In a more specific embodiment, a sensor surface adopted for use with a biosensor is disclosed. The sensor surface comprises a hydrogel matrix coating coupled to a top surface of the sensor surface, wherein the hydrogel matrix coating has plurality of functional groups. At least two different liposomes are bonded to the plurality of functional groups at discrete and noncontiguous locations on the hydrogel mixtrix coating of the sensor surface. In one embodiment, the sensor surface is a sensor chip, and a free electron metal is interposed between the hydrogel matrix and the top surface of the sensor surface.
In another embodiment of this invention, an apparatus is disclosed for assaying a drug candidate, wherein the apparatus comprises a biosensor having one or more sensing surface-bound biomolecules associated therewith and capable of measuring at least one binding interaction parameter of the drug candidate, and a computer memory containing a data structure for comparing the at least one binding interaction parameter against at least one mathematical expression correlated from binding interaction data associated with known drug compounds to determine an estimate of at least one pharnacokinetic parameter of the drug candidate.
In yet a further embodiment, a computer memory containing a data structure useful for assaying a drug candidate in accordance with the methods of the present invention is disclosed (as well as a generated data signal conveying the same). The data structure may be used to determine an estimate of at least pharmacokinetic parameter of the drug candidate.
These and other aspects of the present invention will be evident upon reference to the following detailed description and related Figures.